Open Transaction Network Threat Detection With Multi-Layer Graph Analysis
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Solution Overview
Problem
Open transaction networks are vulnerable to threats such as unauthorized access, fraudulent transactions, and malicious activities, which can result in financial harm and reputation damage, and existing security measures are inadequate in addressing these issues efficiently and dynamically.
Innovation Solution
A scalable threat management system and framework that employs functional modules like multi-lingual authentication, sensitive data minimization, profile detection, cross-border data monitoring, and transaction monitoring, utilizing artificial intelligence and neural networks to identify and mitigate potential threats in real-time across multiple network layers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing security measures are used in open transaction networks, then basic protection is provided, but they are inadequate in addressing threats efficiently and dynamically
Solution Approach 1:
The system dynamically adjusts security measures based on real-time threat assessment. The threat management system continuously monitors transaction patterns, updates risk profiles, and adapts authentication requirements according to the current threat landscape, transforming static security rules into dynamic responses that evolve with emerging threats.
Solution Approach 2:
The system implements feedback loops where transaction outcomes, threat detections, and system performance data are continuously fed back into the threat management module. This feedback mechanism enables the system to learn from past security events, refine its threat models, and improve its response effectiveness over time, addressing the inadequacy of existing measures.
2Reliability
If comprehensive threat monitoring is implemented across multiple network layers, then security coverage is improved, but system complexity increases
Solution Approach 1:
The threat management system is segmented into distinct functional modules including authentication module, data minimization module, profile detection module, cross-border monitoring module, and transaction monitoring module. Each module operates independently at specific network layers, managing its own security functions while interacting through standardized interfaces, thus reducing overall system complexity despite comprehensive coverage.
Solution Approach 2:
The system employs a universal threat management framework that can be applied across multiple network layers (application, presentation, transmission) without requiring separate specialized systems for each layer. The same core algorithms and data structures serve multiple security functions, reducing redundancy and simplifying the overall architecture while maintaining broad security coverage.
3Reliability
If real-time threat evaluation is performed continuously, then threat detection capability is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-computing risk profiles, authentication thresholds, and threat detection rules before actual transactions occur. Historical data is used to establish baseline behaviors and risk patterns in advance, enabling the system to make rapid real-time decisions without performing complex calculations during critical transaction moments, thus reducing processing time while maintaining detection capability.
Solution Approach 2:
The system applies partial action by focusing computational resources on high-risk transactions and patterns rather than uniformly processing all transactions with full analytical depth. Low-risk transactions undergo streamlined processing, while only transactions triggering risk thresholds receive comprehensive real-time evaluation, balancing detection capability with processing time requirements.
Data Source
AI summary
Systems and methods for managing threats in a network including applying, at a first layer, a first request obtained from a first computing device to a first graph to determine one or more first embeddings and one or more second embeddings, classifying, at a second layer, a second request obtained from a second computing device as a first request type or a second request type based on applying the second request to a second graph, predicting, at a third layer, an authenticity of a call sequence obtained from the second computing device based on a sequence threshold, and sending the call sequence to a third computing device based on authenticating the call sequence. The first request is a transaction to be performed by the second computing device and the second request is a second processing transaction to be performed by the third computing device based on the first request.